Digital twin model inversion for testing

ABSTRACT

Creation and use of a digital twin instance (DTI) for a physical instance of the part. The DTI may be created by a model inversion process such that model parameters are iterated until a convergence criterion related to a physical resonance inspection result and a digital resonance inspection result is satisfied. The DTI may then be used in relation to part evaluation including through simulated use of the part. The physical instance of the part may be evaluated by way of the DTI or the DTI may be used to generate maintenance schedules specific to the physical instance of the part.

RELATED APPLICATIONS

The present application is related to PCT Application No.PCT/US2019/031024 filed 7 May 2019, entitled “RESONANCE INSPECTION OFMANUFACTURED PARTS WITH WITNESS COUPON TESTING” which is specificallyincorporated by reference for all that it discloses and teaches.

The present application is also related to U.S. patent application Ser.No. 13/278,380 filed 21 Oct. 2011, entitled “UTILIZING RESONANCEINSPECTION OF IN-SERVICE PARTS” and U.S. Pat. No. 9,157,788 filed 19Jun. 2012, entitled “PART EVALUATION SYSTEM/METHOD USING BOTH RESONANCEAND SURFACE VIBRATION DATA” both of which are specifically incorporatedby reference for all that they disclose and teach.

The present application is a U.S. National Stage Application ofPCT/US2020/041391, filed on Jul. 9, 2020, which claims benefit ofpriority to U.S. Provisional Application No. 62/872,548 filed 10 Jul.2019, entitled “DIGITAL TWIN MODEL INVERSION FOR TESTING,” which isspecifically incorporated by reference for all that it discloses andteaches.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under a Materials andManufacturing Directorate (AFRL/RX) Structural Materials Broad AgencyAnnouncement (BAA) Contract FA8650-15-C-5208 awarded by the U.S. AirForce Research Laboratory (AFRL). The government has certain rights inthe invention.

BACKGROUND

Non-destructive testing of parts provides valuable information that maybe used to evaluate parts. Because non-destructive testing does notdegrade the performance a part, approaches in which every part that isproduced is subjected to non-destructive testing are realized to assistin quality control processes for parts. Often parts subjected tonon-destructive testing are highly scrutinized parts such as those usedin the aerospace industry or the like.

Non-destructive testing may include resonance inspection of a part.Resonance inspection may include resonance ultrasound spectroscopy (RUS)in which a part is subjected to a sweep of input frequencies. The part'sresonance response to the input frequencies may be measured. Theresonance response provides resonance inspection results that may beevaluated to make a determination about the part (e.g., whether the partis compliant to predetermined standards or is not compliant topredetermined standards).

While the use of non-destructive testing such as RUS provides advantagesto part evaluation and testing, the need persists to provide improvedtesting approaches to more accurately determine flaws or faults inparts, determine a useful lifespan of a part, determine if a part isaging appropriately, or other determinations related to a part.

SUMMARY

The present disclosure includes a method for model inversion of adigital model of a part to create a digital twin instance (DTI) thatrepresents a physical instance of the part. The method includesinputting evaluation model parameters to the digital model andconducting one or more convergence digital analyses on the digital modelusing the evaluation model parameters to obtain an evaluation digitalresonance inspection result based on the evaluation model parameters.The method also includes determining convergence parameters from theevaluation model parameters that result in the evaluation digitalresonance inspection result that satisfies at least one convergencecriterion relative to a physical resonance inspection result of thephysical instance of the part. The convergence criterion may be at leastin part based on matching resonance peaks based on correspondingvibrational mode shapes between the evaluation digital resonanceinspection and the physical resonance inspection result. The method alsoincludes assigning the convergence parameters to the digital model todefine a DTI specific to the physical instance of the part.

The DTI of the physical instance of the part may facilitate furtherevaluation of the physical instance of the part. The DTI may be afaithful digital representation of the physical instance of the partthat includes agreement in dimension, material state, and the like. Assuch, the DTI may be further used in evaluation of the specific physicalinstance of the part associated with the DTI. This may includeevaluation of the physical instance of the part prior to being put intoservice by, for example, performing digital analysis of the DTI todetermine if the physical instance of the part is projected to performas expected or to minimum performance standards.

The DTI may also be used in relation to the specific physical instanceof the part to create a tailored maintenance schedule for the specificphysical instance of the part that may consider factors such as thematerial properties of the physical instance of the part, operationaluse parameters of the part, and environmental parameters in which thepart has been or is projected to be used. Other evaluation of thephysical instance of the part may be facilitated using a DTI for thephysical instance of the part or in a DTI system including one or moreDTIs of physical instances of parts as will be described in greaterdetail below.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

Other implementations are also described and recited herein.

BRIEF DESCRIPTIONS OF THE DRAWINGS

FIG. 1 depicts an example of an environment for creation of a digitaltwin instance of a physical instance of a part.

FIG. 2 depicts an example of a system for performing convergenceanalysis to create a digital twin instance of a physical instance of apart.

FIG. 3 depicts example operations for creation of a digital twininstance for a physical instance of a part.

FIG. 4 depicts an example of information exchange that is facilitatedbetween a physical instance of a part and a digital twin instance of thephysical part.

FIG. 5 depicts example operations for use in evaluating an in-servicepart using a digital twin instance of the in-service part.

FIG. 6 depicts example operations for use in determining a maintenanceschedule for a specific physical instance of a part using an associateddigital twin instance.

FIG. 7 depicts an example physical resonance inspection result and anexample digital resonance inspection result prior to model convergence.

FIG. 8 depicts an example physical resonance inspection result and anexample digital resonance inspection result after model convergence.

FIG. 9 depicts example digital resonance inspection results thatindicate a new part, a part in need of service, and a damaged part inneed of repair.

FIG. 10 depicts example digital resonance inspection results thatillustrate the effect of various operational conditions for a part onresonance inspection results for the part.

FIG. 11 depicts example resonance inspection results that illustrate theeffect of part creep on the resonance inspection results for the part.

FIG. 12 depicts an example schematic of a processing device suitable forimplementing aspects of the described technology.

FIG. 13 depicts an example of a plot including a plurality of differentperformance curves that are generated based on simulations of a digitaltwin instance of a physical instance of a part.

DETAILED DESCRIPTIONS

The present disclosure generally relates to generation and use of adigital twin instance (DTI) of a physical instance of the part. Digitalmodels of parts allow for a number of advantages in relation to creationand use of parts. However, often digital models represent a nominalversion of a part. Such a nominal version of the part may not consideror improperly consider real-world effects associated with a physicalimplementation of the nominal part. For example, real, physical partsmay have variations in dimensions, material state, or othercharacteristics relative to the nominal part that are created bymanufacturing tolerances or the like. Moreover, and especially in highlyengineered parts used in high performance or high demand scenarios, theeffect of a material state of the parts may be significant. Digitalmodels that do not account for these variations between a nominalmodeled part and an actual physical instance of the part may lead todiscrepancies between real-world performance of the part and modeleddigital performance of the part. In turn, a factor of safety or otherconservative modeling assumptions may be utilized that are applied toall physical implementations of the nominal part. As can be appreciated,such global treatment of parts may result in cost and timeinefficiencies as the highest performing parts may be treated equallywith the lowest performing parts of a given nominal part definition.

In turn, creation and use of a DTI may create a statistically proven,faithful representation of a physical instance of a part that may takeinto account specific dimensions, material state, or other parametersrelated to the physical instance of the part. The specific physicalinstance of the part may then be evaluated digitally using the DTI,which may enhance the evaluation of the physical instance of the part.Accordingly, the following disclosure generally presents a discussion ofcreation of the DTI for a given physical instance of a part.Additionally, the present disclosure presents a number of approachesfacilitated by a DTI of a physical instance of a part.

FIG. 1 depicts an example environment 100 for creating a DTI of aphysical part. A physical instance 110 of a part may be provided. Also,a digital model 120 of the part may be provided. The digital model 120is a nominal representation of a part as designed. For instance, thedigital model 120 may comprise a three-dimensional computer-aideddrafting (CAD) model of a part. In at least some examples, the CAD modelmay be further represented as a finite element model of the part tofacilitate application of a digital analysis such as finite elementanalysis (FEA) as described in greater detail below. The digital model120 has nominal dimensions of an idealized part created in a digitalenvironment. The physical instance 110 of the part is a physicalembodiment of the nominal part represented by the digital model 120. Asdescribed above, the physical instance of the part may includevariations associated with one or more manufacturing techniques, rawmaterial limitations, or other factor that creates disparity between thenominal part represented by the digital model 120 and the physicalinstance 110 of the part.

For example, the digital model 120 may define a nominal shape andnominal dimensions of the part. The digital model 120 may be used togenerate plans for manufacture of the part. The physical instance 110 ofthe part may be manufactured using any appropriate manufacturingtechnique. Examples may include casting operations, forging operations,machining operations, additive manufacturing operations, extrusionoperations, crystal growth operations, or any other appropriatetechnique for manufacturing.

Regardless of the manufacturing technique(s) used to produce thephysical instance 110 of the part, discrepancies between the digitalmodel 120 and the physical instance 110 of the part may exist. Suchdiscrepancies may include dimensional variances (e.g., even within anacceptable tolerance applied to the physical instance 110 relative tothe nominal part), material state variances (e.g., including potentialdifferences in stress state, integrity, crystal orientation, isotropy,or material homogeneity between the physical instance 110 and thedigital model 120), or the like. In this regard, each individualphysical instance 110 of the nominal part defined by the digital model120 may have different performance characteristics relative to thenominal part. These differences in performance characteristics may besignificant enough to alter part suitability even for parts withinindividual tolerance values established for a physical instance 110relative to the nominal part.

Accordingly, the environment 100 depicted in FIG. 1 may be used togenerate a digital twin instance (DTI) 128, which is a particulardigital model that individually relates to a corresponding physicalinstance 110 of a part. As such, a unique DTI may be created for eachpart produced. Initially, upon production of a physical instance 110 ofa part, the physical instance 110 of the part may be measured usinghighly precise and accurate measuring techniques. This may includehand-measurement, semi-automated measuring, or automated measuringtechniques. For example, structured light scanning may be used todetermine actual part dimensions for the physical instance of the part.These highly accurate measurements may be provided to the digital model120 such that the nominal dimensions of the part are updated with theactual specific dimensions for a given physical instance 110 of thepart. This refinement of the dimensions of the DTI 128 may occur priorto the convergence optimization performed by the convergence module 126described below. Alternatively, actual part dimensions may be determinedusing a convergence approach described in greater detail below.

Additionally, the physical instance 110 of the part may undergoresonance inspection testing in a resonance testing apparatus 112. Inturn, the resonance testing apparatus 112 may generate physicalresonance inspection result 114 that is specific to the physicalinstance 110 of the part. One example of such a physical resonanceinspection result 114 is depicted in FIG. 7. FIG. 7 depicts a frequencydomain plot 700 of a frequency response 702 of the physical part. Ofnote, the frequency response 702 may include resonance frequency peaksat different respective frequencies. Specifically, a first resonancepeak 704, a second resonance peak 706, and a third resonance peak 708are shown. Each resonance peak 704-708 of the frequency response 702 maybe associated with a vibrational mode shape. The vibrational mode shapesmay be determined using surface vibrational analysis or any otherappropriate technique for identifying mode shapes in the frequencyresponse 702. In the example depicted in FIG. 7, the first resonancepeak 704 may be associated with a torsional mode shape, the secondresonance peak 706 may be associated with an extension mode shape, andthe third resonance peak 708 may be associated with a bending modeshape. As will be described in greater detail below, identification ofthe mode shape associated with each resonance peak may be used to matchcorresponding resonance peaks in modeled digital resonance results suchthat resonance peaks having common vibrational modes may be properlycompared between the digital model and the physical instance of thepart.

With return to FIG. 1, the digital model 120 of the part may undergodigital part analysis 122 to generate a digital resonance inspectionresult 124. The digital part analysis 122 may include a simulated,digital resonance inspection of the digital model 120. The digitalresonance inspection may include a finite element analysis (FEA) thatcharacterizes the digital model's free-free modal response, or how thedigital model 120 responds to vibrational inputs corresponding to thefrequency sweep used in a physical resonance inspection of the physicalinstance of the part. The simulated, digital resonance inspection mayalso include forced-response modeling.

The digital part analysis 122 may be any appropriate analyticaltechnique applied to the digital model 120. Such analytical techniquemay be applied using a computer program such as a CAD program or thelike. In an example, the digital analysis 122 includes a FEA approachapplied to the digital model 120. In turn, the resonance response 712 ofthe digital model 120 may be measured in response to simulated inputvibrations corresponding to the input vibrations of the resonancetesting apparatus 112. One example of such a digital resonanceinspection result 124 is depicted in FIG. 7. FIG. 7 depicts a digitalfrequency domain plot 710 of a digital frequency response 712 of thedigital model of the part. Of note, the digital frequency response 712may also include resonance frequency peaks at different respectivefrequencies. As can also be appreciated in FIG. 7, each resonancefrequency peak of the frequency response 712 may also be associated withan identified vibrational mode shape. The vibrational mode shapes may bedetermined based on the part response modeled in the FEA or any otherappropriate technique for identifying mode shapes in the digitalfrequency response 712. The digital resonance response 712 includes afirst resonance peak 714, a second resonance peak 716, and a thirdresonance peak 718. Like in the physical resonance response 702, theresonance peaks 714-718 in the digital resonance response 712 may haveassociated vibrational modes that are identified during the digital partanalysis 122. For example, the first resonance peak 714 is associatedwith a torsional mode shape, the second resonance peak 716 is associatedwith an extension mode shape, and the third resonance peak 718 isassociated with a bending mode shape.

In turn, the physical resonance inspection result 114 and the digitalresonance inspection result 124 may be provided to a convergence module126. As can be appreciated in FIG. 7, the resonance response 702 maydiffer from the digital resonance response 712. This may indicate errorin assumed digital model parameters used in the digital part analysis122 used to create the digital frequency response 712. Of note, theresonance peaks 704-708 in the resonance response 702 may be compared toresonance peaks 714-718 in the digital resonance response 712 such thatresonance peaks for corresponding or common mode shapes are compared forevaluation. That is, as can be seen in FIG. 7, the third resonance peak708 for of the frequency response 702 generally aligns with the secondfrequency resonance peak 716 for the digital frequency response 712.However, these are not common vibrational modes as the second frequencyresonance peak 716 is an extension vibrational mode and the thirdresonance peak 708 is a torsional vibrational mode. As such, withoutidentification of the mode shape for each resonance peak, correspondingresonance peaks may not be accurately compared, thus resulting in errorsbetween the model and the physical part. In the illustrated example,when comparing appropriate resonance peaks 704 and 714 associated with atorsional vibrational mode, resonance peaks 706 and 716 associated withan extension vibrational mode, and resonance peaks 708 and 718associated with a bending vibrational mode, it is clear that significanterror exists between the physical resonance response 702 and the digitalresonance response 712.

As will be described in greater detail below, a convergence module 126may manipulate model parameters for the digital model 120. The digitalmodel 120 may undergo further digital part analysis 122 using modifiedmodel parameters. The modified model parameters are referred to asevaluation model parameters as these parameters are evaluated anditerated until the physical resonance inspection result 114 and thedigital resonance inspection result 124 satisfy at least one convergencecriterion. In other examples, a plurality of convergence criteria may besatisfied prior to convergence. In any regard, the model parameters thatresult in convergence are designated as convergence model parameters,which are assigned to the digital model 120 to define the DTI 128 forthe physical instance 110.

The one or more convergence criterion may, in an example, relate to thecorrespondence between frequency resonance peaks in the physicalresonance result and the digital resonance result. As such, the resultsin FIG. 7 provide an example that does not satisfy the convergencecriterion. In any regard, the convergence module 126 may be operative tovary evaluation model parameters and compare the digital resonanceresponse for each set of modified evaluation model parameters to thephysical resonance inspection result 114. This may be repeated untilconvergence is achieved. With further respect to FIG. 8, a frequencydomain plot 800 includes a physical frequency response 802. FIG. 8 alsoincludes a digital frequency domain plot 810 that includes a digitalfrequency response 812. As can be appreciated, convergence may besatisfied by these results as the frequency resonance peaks for eachcorresponding mode shape in each response 802 and 812 may correspond towithin a predefined frequency threshold.

Accordingly, the DTI 128 may be particular to a given physical instance110. For example, each physical instance 110 of the part may have anassigned serial number for use in tracking specific individual physicalinstances 110 of the part. A DTI 128 may be generated for each physicalinstance 110 of the part. As such, the DTI 128 for a given physicalinstance 110 may have a digital serial number. The digital serial numbermay be the same as a serial number for the physical instance 110 of thepart. Alternatively, the digital serial number for a DTI 128 may beuniquely associated with the serial number for the physical instance110. In any regard, the DTI 128 may be specific to a given physicalinstance 110 of a part such that a unique correspondence between the DTI128 and the physical instance 110 is provided.

FIG. 2 depicts a more detailed example of a system 200 for use ingenerating a DTI 128 for a physical instance 110 of a part. The system200 includes an inversion optimization module 226. Assumed modelparameters 202 may be provided to the inversion optimization module 226.In turn, the inversion optimization module 226 may use the assumed modelparameters 202 with a digital model 220 comprising a nominal partrepresentation of a physical instance of a part or a dimensionallycorrected digital model of the part. The digital model 220 may undergodigital resonance inspection (e.g., using an FEA approach or the like)on the basis of the assumed model parameters 202 to provide a digitalresonance result 224. The digital resonance result 224 is compared to aphysical resonance result 214 obtained from a resonance inspection of aphysical instance of the part in a residual error calculation 204.

The residual error calculation 204 may represent a quantification of theresidual error between the physical resonance result 214 and the digitalresonance result 224. For instance, continuing the example depicted inFIG. 7, residual error may represent differences in frequencies ofresonance peaks of corresponding vibrational mode shapes between thephysical frequency response 702 and the digital frequency response 712.Error may also be determined relative to relational values (e.g., thespan of frequencies between resonance peaks) between respectivefrequency resonance peaks for different modes. The residual errorcalculation 204 may include differences in amplitude, damping, or phaseas well. In an example, the residual error calculation 204 may be theroot-mean-square of the residual errors (RMSE) between the physicalresonance result 214 and the digital resonance result 224.

The residual error calculation 204 may be provided to the inversionoptimization module 226 to determine if one or more convergence criteriaare satisfied as represented by the residual error calculation 204. Thatis, the convergence criterion may relate to a residual error value belowa threshold. Alternatively, the convergence criterion may relate to athreshold differential between subsequent iterations of the assumedmodel parameters 202. If the convergence criterion is not met, theinversion optimization module 226 changes the assumed model parameters202. The changed assumed model parameters 202 may be referred to asevaluation model parameters, which may be iterated until a convergencecriterion has been met.

The evaluation model parameters may be generated by varying the modelparameters in any appropriate manner. As may be appreciated, the modelparameters may be multidimensional with any appropriate number ofdimensions to accurately reflect applicable model parameters. Examplesof model parameters may relate to material state, material orientations,physical dimensions, or the like. For example, the material stateincluded in the input model parameters may be reflected in one or moreparameters related to one or more modulus values (e.g., elastic modulus,plastic modulus, Young's modulus), Poisson's ratio, Zener's anisotrophyratio, mass, density, stress state, integrity, isotropy, homogeneity, orother material property. The material orientation parameters may includeone or more values related to orientation of a grain or crystalstructure within the part. This may include angles relative to one ormore part datums or angles of rotation of the grain or crystalstructure. Physical dimensions may also be included in the modelparameters. In turn, a model parameter be any appropriate physicaldimension of the part. In at least one example, the physical dimensionsmay be set parameters based on measured values for the physical instanceof the part. The model parameters may also include stress stateinformation for the part.

The input model parameters may be varied to test any number ofcombinations of parameters within a multidimensional distribution. Forinstance, random sampling, Latin hypercube sampling, or orthogonalsampling may be applied to vary the evaluation input model parametersfor use in iterations to achieve convergence and determine convergencemodel parameters. Further still, some model parameters may be fixed(e.g., if an actual part dimension is explicitly measured as describedabove).

Accordingly, the inversion optimization module 226 may continue toiterate within the multidimensional distribution representing theevaluation model parameters. Once a set of evaluation model parametersare identified that result in convergence between the physical resonanceresult 214 and the digital resonance result 224, the inversionoptimization module 226 may output these model parameters as convergencemodel parameters 206. The convergence model parameters 206 may beassigned to the digital model 220 to create a DTI for a given physicalinstance of a part.

FIG. 3 depicts example operations 300 for model inversion. A generatingoperation 302 generates a digital model of a part. The digital model maybe a nominal part specification representing a nominal version of thepart with respect to one or more of part dimensions or material state. Aproducing operation 304 produces a physical instance of the partrepresented by the digital model. The producing operation 304 mayinclude any appropriate manufacturing or fabrication technique, such asthose described above or others.

A performing operation 306 includes performing resonance inspection ofthe physical instance of the part. The performing operation 306 resultsin the generation of a physical resonance result for the physicalinstance of the part produced in the producing operation 304. Thephysical resonance result may be a resonance response of the physicalinstance of the part to a plurality of input frequencies. For example,the resonance response may include a frequency domain representation ofthe resonance response of the physical part in which resonancefrequencies are identified as resonance peaks in a frequency curve inthe frequency domain. In an example, the physical resonance result mayalso identify vibrational mode shapes associated with each resonancepeak in the resonance response. Surface vibration measurement may beused to identify a mode shape for a given resonance peak in the physicalresonance result.

An inputting operation 308 inputs assumed model parameters to thedigital model of the part. The assumed model parameters may be selectedat random or using any approach outlined above to generate the assumedmodel parameters. Also, the model parameters may be any of theparameters identified above related to material state, materialorientation, physical dimensions or the like. A performing operation 310performs a digital resonance inspection of the digital model using theassumed model parameters.

A comparing operation 312 compares the physical resonance result fromthe physical resonance inspection with the digital resonance result ofthe digital resonance inspection. The comparing operation 312 mayinclude determining a residual error between the physical resonanceresult and the digital resonance result based on frequency error betweencorresponding resonance peaks having corresponding vibrational modes.The residual error may reflect differences between resonance peaks inthe physical resonance result and the digital resonance result. Inaddition, the residual error may also reflect different amplitudes,phase, or other measure of resonance peaks between the physicalresonance result and the digital resonance result. Further still, theresidual error may reflect differences in relative resonance metricsbetween the physical resonance result and the digital resonance result(e.g., a comparison of a spread between a first resonance peak and asecond resonance peak in the physical resonance result to acorresponding spread between a first resonance peak and a secondresonance peak in the digital resonance result).

In any regard, an assigning operation 314 assigns evaluation modelparameters to the model. This may involve varying one or more of themodel parameters from the assumed model parameters. Selection of theevaluation model parameters may include any appropriate approach toselection of parameter values from a multidimensional distribution whereeach dimension represents a respective model parameter.

A performing operation 316 performs a digital resonance inspection ofthe digital model with the evaluation model parameters assigned in theassigning operation 314. In turn, a determining operation 318 determinesif a convergence criteria is satisfied by the evaluation modelparameters. The convergence criteria may be based on a residual errorbetween the physical resonance result and the digital resonance result.If the convergence criterion is not satisfied, operation 318 may returnto the assigning operation 314 such that new evaluation model parametersare assigned to the digital model. Operation may iterate over theassigning operation 314, performing operation 316, and determiningoperation 318 until the one or more convergence criterion is satisfied.Once the convergence criterion is satisfied, an assigning operation 320assigns the evaluation module parameters that result in the convergencecriterion being satisfied to the digital model to generate a DTI of thedigital model for the physical instance of the part produced in theproducing operation 304.

Once the model inversion has been performed to generate a DTI 128 for aphysical instance 110 of a part, the DTI 128 may be used in a number ofrespects for evaluation of the physical instance 110 of the partrepresented by the DTI 128. Some examples of which are described ingreater detail below. In general, use of the DTI 128 may allow varioussimulated conditions may be digitally applied to the DTI 128 forevaluation of how the physical instance 110 of the part behaves underthe simulated conditions. As may be appreciated, generation and testingof the simulated conditions in a digital environment applied to the DTI128 may provide advantages over physically testing the physical instance110 of the part.

In a first regard, the digital testing or simulation applied to the DTI128 may allow for testing to failure in the digital environment withoutactually destroying the physical instance 110. Performance boundariesmay be established for the DTI 128, including performancecharacteristics of the DTI 128 leading up to failure. For instance, afailure analysis may be conducted on the DTI 128 to verify performanceof the physical instance of the part without having to subject thephysical instance of the part to testing to failure. In turn, one ormore digital performance standards may be provided to which the DTI 128may be compared. As described above, the digital performance standardmay relate to ultimate performance of a part, performance within somedefined range, or in-service performance. In any regard, a digital sortmay be used in which the results of testing of the DTI 128 are comparedto the digital performance standards to characterize the DTI 128, and inturn, the physical instance 110.

Furthermore, digital simulation may allow for a simulated elapsed timeto be modeled in an accelerated time period. This digital modeling maybe performed more quickly that accelerated physical testing may occur.For instance, part performance may be simulated for a very long span ofpart use in an accelerated manner. As such, years of use of the part maybe simulated using the DTI 128 in a much compressed time frame (e.g., inminutes or hours). In this regard, simulation of the operation of thepart and the resulting performance characteristics of the part may bevery quickly produced. Such simulation may allow for evaluation of thesuitability of the physical instance 110 of the part for application incertain conditions or environments. Such simulated use may be performedmore quickly than traditional part qualification testing.

Additionally or alternatively, maintenance schedules may be generatedbased on the simulated part usage such that specific maintenanceintervals may be established on a need basis for individual physicalinstances 110 of the part. Further still, simulated wear or usage of theDTI 128 may allow for evaluation of the physical instance 110 of thepart through the lifespan of the physical instance 110. That is, aphysical resonance result for the physical instance 110 of the part maybe compared to a corresponding digital resonance result for the DTI 128at various points in time during the lifespan of the part to determineif the physical instance 110 is aging in an acceptable or expectedmanner.

As discussed in greater detail below, once a DTI is generated for aphysical instance of a part, the DTI may be used in a number of ways inrelation to evaluation/testing of the physical instance of the part.FIG. 4 illustrates a number of information flows that may be used inrelation to a physical instance of a physical part 410 and a DTI 420 forthe part. For example, physical information 430 collected in relation tothe physical part 410 such as operational history, maintenance history,CAD geometry information, and material state may be imparted to the DTI420. As can be appreciated, the CAD geometry information and materialstate information may be imparted to the DTI 420 in relation to themodel inversion process described above.

The operational history and maintenance history of the physicalinformation 430 provided to the DTI 420 may be actual or modeledinformation regarding the physical conditions to which the part isexposed. For example, the operational history for the physical part 410may be measured as the physical part 410 undergoes service (e.g., in thefield, during testing, etc.). This measured history of the physical part410 may include operational information (e.g., number of cycles,operation time, operation speeds, etc.). The measured history of thephysical part 410 may also include measured environmental parameters towhich the part 410 has been exposed such as temperature or the like.Alternatively, simulated use of the physical part 410 may be provided.This may include operational simulation, load simulation, or simulatedenvironmental factors. For example, estimated environmental parametersmay be attributed to the DTI for evaluation of the DTI 420 and physicalpart 410.

In turn, model information 440 may be used to make determinationsregarding the physical part 410. The model information 440 may includeoperational performance information, defect detection, damageprediction, or recommended maintenance for the physical part 410. Thegeneration and use of this model information 440 generated from the DTI420 is described in greater detail below.

With further reference to FIG. 13, various examples of part performancecurves are shown in a plot 1300. The plot 1300 includes a horizontalaxis representative of time and a vertical axis representative of anoperational capacity ranging from 0% to 100%. The operational capacityindicator may be a remaining life value represented as a percentage of aprojected life span for the part, a percentage of optimal performancedemonstrated by the part, or other metric representative of theperformance of the part. The performance curves 1302-1310 may representvarious potential circumstances associated with a part. In turn, each ofthe performance curves may be determined using simulated usage of a partover time in various ones of the circumstances using a DTI for aphysical instance of a part. In turn, the operational curves 1302-1310may be used to determine appropriate maintenance intervals for a partcorresponding to one of the identified circumstances or a physical partmay be compared (e.g., using comparative resonance inspection) to one ormore of the operational curves to determine an operational condition forthe physical part.

For example, curve 1302 may represent an idealized part having a nominalmaterial state that operates in nominal operational conditions. That is,curve 1302 may be a baseline part representing completely nominal partlife. Curve 1304 represents the part life of a part having amanufacturing defect. Thus, after a brief period of relatively nominaloperation, performance may deteriorate rapidly and prematurely comparedto curve 1302.

Curve 1306 represents a part having lower specification material statescompared to nominal. In this regard, the operational life maydeteriorate prematurely compared to curve 1302, but otherwise degradepredictably. If a part is identified as corresponding to this condition,more regular or frequent maintenance intervals may be established tomonitor the progress of the part. Curve 1308 may represent performanceof a part in a harsh environment such as a high temperature environment,a corrosive environment, or the like. Again, performance of the part maybe relatively nominal for a period with more rapid degradation ofperformance as compared to the nominal part in curve 1302. Again,identification of a part corresponding to curve 1308 may be subjected tomore regular or frequent maintenance.

Finally, curve 1310 may represent a part with exceptional material state(e.g., idealized material state or the like) or a part operating anextremely mild environment. As can be appreciated, performance of such apart may be extended relative to the nominal curve 1302 such thatmaintenance intervals may be extended or less frequency as compared to anominal or less performant part.

FIG. 5 depicts example operations 500 for evaluation of an in-servicepart using a DTI for the part. As can be appreciated, FIG. 5 initiateswith a DTI of a part. In this regard, the operations 500 may rely oncreation of a DTI for the part as described above or may simply beprovided for operation on a DTI for a given part regardless of themanner in which the DTI is generate. An inputting operation 502 inputsmeasured or estimated operational parameters into the DTI for the givenphysical part to be evaluated.

As will be appreciated, any of the operational parameters input to themodel in the inputting operation 502 may be either estimated ormeasured. For example, when providing operational parameters, past orfuture operational parameters related to the performance of the part maybe provided. Such estimated operational parameters may relate tooperational parameters for the part that are anticipated to occur suchthat the estimate is generated prior to the in-service part having beenput into service. These estimates may be used to forecast partperformance (e.g., even prior to the part being put into service).Alternatively or additionally, the estimated operational parameters mayrelate to estimated operational conditions in which the part has beenin-service. In this later regard, in which the in-service part hasundergone service, one or more operational parameters may be actualmeasured values for the conditions experienced by the part. As such, theoperational parameters input to the DTI may be all estimated, allmeasured, or some operational parameters may be estimated, and othersmay be measured. Further still, the measured values may relate to anenvironment in which the part is to be put into service.

The operational parameters may comprise any estimated or measured factorthat relates to part performance. For example, operational parametersmay relate to the use of the part. These values for operational useparameters may include a number of hours of operation of the part, anumber of cycles the part has experienced, or any other informationrelated to the amount the part has been used. The operational parametersmay be estimated operational parameters or actual (e.g., measured)operational parameters.

Other parameters may also be used such as environmental parameters. Theenvironmental parameters may relate to temperatures to which the parthas been exposed, humidity to which the part has been exposed,interaction with chemicals in contact with the part, or the like.

In any regard, the inputting operation 502 may input any appropriateoperational parameters to the DTI. In turn, a simulating operation 504simulates, using the DTI and operational parameters, the use of thephysical instance of the part and the associated effects on the part.The simulating operation 504 may include any appropriate digitalmodeling techniques to provide the simulated use of the part. This mayinclude FEA of the part with simulated loads based on the operationaluse parameters and/or environmental parameters. As an example, a turbineblade for a turbine engine may be the in-service part to be evaluated.In this regard, operational use parameters regarding the number of hoursof use and/or the engine speed may be estimated or measured. In turn,the simulating operation 504 may simulate the effects of such use on thepart to be evaluated. Moreover, environmental parameters such as ambientair intake temperature or the like may be input to the DTI to morefaithfully simulate the actual state of the in-service part using theDTI. As described above, the environmental parameters may be measuredenvironmental parameters or estimated environmental parameters.

In turn, performing operation 506 performs digital resonance inspectiontesting on the DTI in view of the simulated use of the simulatingoperation 504. The performing operation 506 generates a digitalresonance result for the simulated in-service part represented by theDTI. A performing operation 508 performs a physical resonance inspectionon the in-service part. In turn, a comparing operation 510 compares thedigital resonance result for the simulated in-service DTI with thephysical resonance result of the physical instance of the in-servicepart.

A characterizing operation 512 characterizes the physical instance ofthe in-service part in response to the comparing operation 510. Thecharacterizing of the physical instance of the in-service part mayclassify the physical instance of the in-service part as “acceptable” or“unacceptable.” Other descriptors such as a compliant part status or anuncompliant part status may be used. However, a binary classification isnot strictly required. For example, other characterizations may beapplied to the in-service part such as grading of the part relative tomore than two classes. Furthermore, quantitative measures may be appliedsuch as an estimated remaining life of the part.

With further reference to FIG. 9, simulated use of the DTI may allow forresonance results to be generated that may assist in characterization ofa part. For instance, in FIG. 9, the DTI may be subjected to simulationsto create a new part resonance response 900, an in-service partresonance response 910, and a repaired resonance response 920.Additionally, the DTI may be subjected to FEA analysis to determineperformance limits for the physical instance of the part. For instance,the in-service part resonance response 910 may represent a resonanceresult for a part that is in need of service, which is identifiedthrough performance characteristics provided by the DTI. As such, thephysical instance of the part that is in-service may be tested toevaluate the physical resonance result relative to the digital resultsrepresented in FIG. 9 to determine if the physical instance of the partis behaving as expected based on the simulated performance of the DTI.

FIG. 6 depicts example operations 600 for use of a DTI in determining amaintenance interval for an in-service part. The operations 600 includean inputting operation 602 that inputs operational parameters to theDTI. As described above, the operational parameters may be any measuredor estimated operational parameters including operational useparameters, operational environmental parameters, or the like. In turn,a simulating operation 604 simulates the use of the physical instance ofthe part using the DTI for the physical instance of the part. Anevaluation operation 606 evaluates the DTI having undergone simulateduse relative to operation criteria. Such operation criteria may relateto any value or property associated with a part that may requireevaluation for safety or operational performance.

A determining operation 608 determines a maintenance interval for thephysical instance of the part based on the evaluation operation 606. Forexample, traditionally maintenance intervals may be set globally for agiven part without specific consideration to individual physical partproperties or the conditions in which individual parts are operated. Assuch, these maintenance intervals may be designed with a factor ofsafety in view of the worst performing acceptable parts in the mostrigorous environmental conditions. This may result in more or morefrequent maintenance intervals for parts that do not require suchrigorous maintenance (e.g., due to improved individual part performanceor operation in less rigorous environments). By evaluation in theevaluation operation 606 of the DTI for a specific physical instance ofa part in the measured or estimated conditions in which the part isused, a maintenance interval for a specific physical instance of a partmay be established to avoid costly and time consuming over-rigorousmaintenance intervals.

With reference to FIG. 10, a number of resonance response plots 1000,1010, 1020, 1030, and 1040 are depicted. Plot 1000 illustrates aresonance response for a new part. Plot 1010 illustrates a resonanceresponse for a part having low hours or light duty. Plot 1020illustrates a resonance response of a part having high hours or heavyduty. Plot 1030 illustrates a resonance response for a part exposed toover-temperature situations. Plot 1040 illustrates a resonance responsefor a part that has been damaged. As can be appreciated from theseplots, acceptable resonance results may be identified in relation tooperational conditions to which the part is exposed. Such testing of theDTI may allow for determinations to be made for a maintenance intervalfor a given part. Moreover, testing of the physical part may allow thepart to be evaluated in relation to one or more of the conditionsmodeled to create the plots 1000-1040 shown in FIG. 10.

Furthermore, the DTI may be used to model physical changes to a part.For instance, a part may experience creep during use. In turn, the DTIfor a given part may be used to simulate such creep conditions andcreate resonance response results that correspond to different creepconditions. FIG. 11 illustrates such resonance responses. Plot 1100illustrates a baseline or new part response. Plot 1110 illustrates a 2%creep condition, Plot 1120 illustrates a 4% creep condition, plot 1030illustrates a 6% creep condition, and plot 1140 illustrates an 8.8%creep condition. In turn, the plots 1100-1140 illustrated in FIG. 11 mayallow for evaluation of the effect of such creep conditions on the partand may facilitate identification of such conditions in the in-servicephysical instance of the part.

With returned reference to FIG. 6, the operations 600 may includeevaluation of a part relative to the modeled DTI (e.g., during amaintenance interval once established). A performing operation 610performs a resonance inspection of the physical instance of the part togenerate a physical resonance result. A comparing operation 612 comparesthe physical resonance result to a digital resonance result to determineif the physical instance of the part is aging as expected. In turn, twopotential operations may follow the comparing operation 612. In oneexample, a classifying operation 616 classifies the part based on thecomparing operation 612. For example, the part may be classified ascompliant, non-compliant, or some other non-binary classification asdescribed above. Furthermore, a revising operation 614 may revise theDTI, one or more model parameters, or operational parameters based onthe comparing operation 612. For instance, if in-service parts evaluatedusing the operations 600 are consistently variant to the modeledperformance of corresponding DTIs, this information may be used tomodify one or more of the digital techniques described above to improvemodeling accuracy.

Aspects of the foregoing may utilize a computer processing system. Forinstance, operation of the inversion optimization module 226, digitalpart analysis, digital model manipulation, vibrational testingapparatus, or the like may be executed using a computer processingsystem. One example of an appropriate processing device 1200 is shown inFIG. 12. FIG. 12 illustrates an example schematic of the processingdevice 1200 suitable for implementing aspects of the disclosedtechnology. The processing device 1200 includes one or more processorunit(s) 1202, memory 1204, a display 1206, and other interfaces 408(e.g., buttons). The memory 1204 generally includes both volatile memory(e.g., RAM) and nonvolatile memory (e.g., flash memory). An operatingsystem 1210, such as the Microsoft Windows® operating system, theMicrosoft Windows® Phone operating system or a specific operating systemdesigned for a gaming device, resides in the memory 1204 and is executedby the processor unit(s) 1202, although it should be understood thatother operating systems may be employed.

One or more applications 1212 are loaded in the memory 1204 and executedon the operating system 1210 by the processor unit(s) 1202. Applications1212 may receive input from various input local devices such as amicrophone 1234, input accessory 1235 (e.g., keypad, mouse, stylus,touchpad, gamepad, racing wheel, joystick). Additionally, theapplications 1212 may receive input from one or more remote devices suchas remotely located smart devices by communicating with such devicesover a wired or wireless network using more communication transceivers1230 and an antenna 1238 to provide network connectivity (e.g., a mobilephone network, Wi-Fi®, Bluetooth®). The processing device 1200 may alsoinclude various other components, such as a positioning system (e.g., aglobal positioning satellite transceiver), one or more accelerometers,one or more cameras, an audio interface (e.g., the microphone 1234, anaudio amplifier and speaker and/or audio jack), and storage devices1228. Other configurations may also be employed.

The processing device 1200 further includes a power supply 1216. Inturn, the power supply 1216 is powered by one or more batteries or otherpower sources and provides power to other components of the processingdevice 1200. The power supply 1216 may also be connected to an externalpower source (not shown) that overrides or recharges the built-inbatteries or other power sources.

In any of the foregoing examples, one or more components may be executedby a processing device 1200 as depicted in FIG. 12. The processingdevice 1200 may include a variety of tangible processor-readable storagemedia and intangible processor-readable communication signals. Tangibleprocessor-readable storage can be embodied by any available media thatcan be accessed by the processing device 1200 and includes both volatileand nonvolatile storage media, removable and non-removable storagemedia. Tangible processor-readable storage media excludes intangiblecommunications signals and includes volatile and nonvolatile, removableand non-removable storage media implemented in any method or technologyfor storage of information such as processor readable instructions, datastructures, program modules or other data. Tangible processor readablestorage media includes, but is not limited to, RAM, ROM, EEPROM, flashmemory or other memory technology, CDROM, digital versatile disks (DVD)or other optical disk storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, or any othertangible medium which can be used to store the desired information andwhich can be accessed by the processing device 1200. In contrast totangible processor readable storage media, intangible processor-readablecommunication signals may embody processor-readable instructions, datastructures, program modules or other data resident in a modulated datasignal, such as a carrier wave or other signal transport mechanism. Theterm “modulated data signal” means an intangible communications signalthat has one or more of its characteristics set or changed in such amanner as to encode information in the signal. By way of example, andnot limitation, intangible communication signals include signalstraveling through wired media such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared, and otherwireless media.

Some implementations may comprise an article of manufacture. An articleof manufacture may comprise a tangible storage medium to store logic.Examples of a storage medium may include one or more types ofprocessor-readable storage media capable of storing electronic data,including volatile memory or non-volatile memory, removable ornon-removable memory, erasable or non-erasable memory, writeable orre-writeable memory, and so forth. Examples of the logic may includevarious software elements, such as software components, programs,applications, computer programs, application programs, system programs,machine programs, operating system software, middleware, firmware,software modules, routines, subroutines, operation segments, methods,procedures, software interfaces, application program interfaces (API),instruction sets, computing code, computer code, code segments, computercode segments, words, values, symbols, or any combination thereof. Inone implementation, for example, an article of manufacture may storeexecutable computer program instructions that, when executed by acomputer, cause the computer to perform methods and/or operations inaccordance with the described implementations. The executable computerprogram instructions may include any suitable type of code, such assource code, compiled code, interpreted code, executable code, staticcode, dynamic code, and the like. The executable computer programinstructions may be implemented according to a predefined computerlanguage, manner or syntax, for instructing a computer to perform acertain operation segment. The instructions may be implemented using anysuitable high-level, low-level, object-oriented, visual, compiled and/orinterpreted programming language.

The implementations described herein are implemented as logical steps inone or more computer systems. The logical operations may be implemented(1) as a sequence of processor-implemented steps executing in one ormore computer systems and (2) as interconnected machine or circuitmodules within one or more computer systems. The implementation is amatter of choice, dependent on the performance requirements of thecomputer system being utilized. Accordingly, the logical operationsmaking up the implementations described herein are referred to variouslyas operations, steps, objects, or modules. Furthermore, it should beunderstood that logical operations may be performed in any order, unlessexplicitly claimed otherwise or a specific order is inherentlynecessitated by the claim language.

What is claimed is:
 1. A method for model inversion of a digital modelof a part to create a digital twin instance for a physical instance ofthe part, the method comprising: inputting evaluation model parametersto the digital model; conducting one or more convergence digitalanalyses on the digital model using the evaluation model parameters toobtain an evaluation digital resonance inspection result based on theevaluation model parameters; determining convergence parameters from theevaluation model parameters that result in the evaluation digitalresonance inspection result that satisfies at least one convergencecriterion relative to a physical resonance inspection result of thephysical instance of the part, wherein the at least one convergencecriterion is based on comparing corresponding resonance peaks in theevaluation digital resonance inspection result and the physicalresonance inspection result having a common vibrational mode shape;assigning the convergence parameters to the digital model to define adigital twin instance specific to the physical instance of the part;digitally analyzing the digital model of the part based on theevaluation model parameters for the digital model to obtain theevaluation digital resonance inspection result; performing a resonanceinspection test on the physical instance of the part to obtain thephysical resonance inspection result; and comparing the physicalresonance inspection result with the evaluation digital resonanceinspection result.
 2. The method of claim 1, further comprising:measuring one or more measured dimension of the physical instance of thepart; and updating the digital twin instance to reflect the one or moredimension of the measured dimension of the physical instance of thepart.
 3. The method of claim 1, further comprising: applying a digitalsort to the digital twin instance based on a comparison of one or moremodeled performance characteristics for the digital twin instance todigital performance standards for the part; and characterizing thephysical instance of the part in response to the comparing to thedigital sort.
 4. The method of claim 3, wherein the characterizingcomprises assigning at least one of a compliant part status or anon-compliant part status to the physical instance of the part.
 5. Themethod of claim 3, wherein the one or more modeled performanceparameters relate to simulated accelerated wear for the digital twininstance of the part.
 6. The method of claim 3, wherein the one or moremodeled performance parameters comprise failure analysis of the digitaltwin instance.
 7. The method of claim 1, further comprising: simulatingchanges of the physical instance of the part resulting from use of thepart using the digital twin instance of the physical instance of thepart.
 8. The method of claim 7, further comprising: creating amaintenance schedule for the physical instance of the part at least inpart based on the modeled changes.
 9. The method of claim 7, furthercomprising: performing an in-service resonance inspection of thephysical instance of the part after the physical instance of the parthas been put into service to obtain a physical in-service resonanceinspection result; comparing the physical in-service resonance result tothe modeled changes of the digital twin instance; and characterizing thephysical instance of the part that is in-service in response to thecomparing.
 10. The method of claim 9, further comprising: comparing thephysical in-service resonance inspection result with physical resonanceinspection result of the part as new to determine a change to thephysical part; and wherein the comparing the physical in-serviceresonance result to the modeled changes of the digital twin instancecomprises comparing the change to the physical part to the modeledchanges.
 11. The method of claim 9, wherein the characterizing operationcomprises determining the physical instance of the part that isin-service is in a non-compliant status, the method further comprising:updating a new part sort applied to new parts to determine a status ofthe new parts based on resonance results of the physical instance of thein-service part.
 12. The method of claim 11, wherein the characterizingoperation further comprises applying an in-service sort based on thephysical in-service resonance inspection result and the modeledin-service digital twin instance.
 13. The method of claim 12, whereinthe in-service sort is based on modeled changes from a plurality ofmodeled in-service digital twin instances.
 14. The method of claim 12,wherein the in-service sort is based on a plurality of physicalresonance inspection results for a plurality of in-service parts. 15.The method of claim 12, wherein the in-service sort is based on aplurality of parts having undergone qualification testing.
 16. Themethod of claim 9, further comprising: updating the digital model basedon the comparing operation.
 17. The method of claim 7, wherein thesimulating operation further comprises: providing one or moreoperational parameters to the digital twin instance regarding operationof the physical instance of the part.
 18. The method of claim 17,wherein at least one of the one or more operational parameters aremeasured from the operation of the physical instance of the part. 19.The method of claim 17, wherein at least one of the one or moreoperational parameters are estimated operational parameters for theoperation of the physical instance of the part.
 20. The method of claim17, wherein the one or more operational parameters comprise at least oneof an operational use parameters or an environmental parameter.
 21. Themethod of claim 20, wherein the environmental parameter describes anenvironment in which the physical instance of that has been in-service.22. The method of claim 21, wherein the environmental parameterscomprise measured environmental parameters.
 23. The method of claim 21,wherein the environmental parameters comprise estimated environmentalparameters.
 24. The method of claim 1, wherein the determining at leastincludes comparing a first resonance peak in the digital resonance inevaluation digital resonance inspection result and a second resonancepeak in the physical resonance inspection results relative to theconvergence criterion, and wherein the first resonance peak and thesecond resonance peak are identified as having a common vibrationalmode.